Skip to main content
Premium Trial:

Request an Annual Quote

Early Data from UChicago's 1,200 Patients PGx Project Show High Physician Adoption

Premium

NEW YORK (GenomeWeb) — Early data from the University of Chicago's 1,200 Patients Project — a preemptive PGx genotyping feasibility study — suggests there is strong interest among physicians and patients for the routine incorporation of PGx information in heatlhcare decision making.

During a session at the American Society of Clinical Oncology's annual meeting, Peter O'Donnell, principal investigator, presented data from the study launched in 2011. The researchers aim to assess whether physicians with access to previously recorded patient genotypes would actually use PGx information in the routine healthcare setting.

The hypothesis underlying the study is that testing patients for key PGx variants preemptively and making that information available to physicians in an electronic web-based format could overcome the oft-cited drawback that waiting for genetic test results takes too long for them to be clinically useful. The Chicago group has partnered with Oregon Health & Sciences University to perform the study using a custom panel on the Sequenom MassArray system, which O'Donnell said covers both the full spectrum of known PGx variants, as well as variants that the team believes have the potential to be validated in the near future.

At the conference, O'Donnell said the project has recruited a total of 981 patients among a total of 1,135 subjects that researchers initially approached to join. Interpreted another way, approximately 86 percent of patients asked to join the study have given their consent. Of these 981, over 700 have been genotyped for the markers of interest.

More than halfway to its goal of enrolling 1,200 subjects, the study has also begun to collect data on how access to patients' electronically stored PGx information is impacting physicians' practices.

The study has recruited 17 physicians across different specialties, each of whom has then recruited his or her own patients into the study. The cohort includes seven primary care physicians, three oncologists, and two cardiologists, as well as single doctors from several other areas. Primary care doctors have brought in the most patients, 333, followed by 183 patients in cardiology and 147 in oncology.

O'Donnell said in his presentation that the most common disease being treated in recruited patients is hypertension, followed by hyperlipidemia. Cancers, cardiovascular disease, and other disorders have made up a lower percentage of the cohort, he added.

To gauge whether patients' PGx data are impacting physician's decision making, researchers have been tracking doctors' use of the project's Genomic Prescribing System, or GPS, when they interact with patients after being genotyped. O'Donnell reported that there have been more than 600 of these clinic encounters within the study so far and the researchers have found that for about 85 percent of these meetings, physicians have accessed the GPS, physically logging into the system.

In developing the GPS, the Chicago team believed physicians would need an easy and quickly recognizable way to understand whether a PGx association was present for their patient or not. To achieve this, they designed the system so that for each drug a patient is prescribed with a potential PGx association the report includes a graphic representation of a traffic light with either a red, yellow, or green signal.

A green light means the genetics are favorable, in other words, that there are no PGx variants present that would confer increased risk of toxicity or lower likelihood of efficacy. Yellow lights indicate an intermediate risk, and red lights represent a very low chance of response or a very high risk of toxicity or adverse reactions. A column next to the traffic signals reports the level of evidence for the PGx association from level one, indicating multiple studies supporting the link, to level three, indicating a lower level of evidence in the scientific literature.

A final column lists Rx alternatives, each with their own traffic light signal based on the patient's genotype.

So far, the GPS has returned a total of 948 signals, O'Donnell said in his presentation. Only 11 have been red lights, and there have been 335 yellow lights and 476 green lights.

That the majority has been green lights has been interesting, he added, because it suggests that "most of the time physicians and patients have already found their way to the green light drugs."

From these basic red light, green light, yellow light reports, the GPS also allows physicians to access more detailed information by clicking on a relevant light signal. This brings them to what the group calls a "30-second summary," explaining the clinical significance of the PGx variant and giving alternate prescribing information in more detail.

So far in the study, O'Donnell said that doctors have clicked through to the 30-second summary for 100 percent of the red lights, about 75 percent of the yellow lights, and only 20 percent of the time when a green light is indicated.

The researchers have also looked at how often physicians change a treatment or prescription based on the GPS, and have found that thus far, doctors changed their treatment plans in one-third of the red light cases, about 8 percent of the yellow light cases, and 4 percent of the green light cases.

"People ask me why it's not 100 percent in the red light cases," O'Donnell said at the meeting. "And what we have seen is that, so far, these red lights are all for proton pump inhibitors, predicting decreased response. But physicians have told us that for the patients they didn’t change, it was because they said they were doing well" on their current PPI.

The GPS, he said, is just one tool among many that doctors can use to inform their prescribing, and it is obviously being weighed against other factors like patient's self-reported outcomes and symptoms.

Most interesting, O'Donnell said, is the difference the group has seen between how often green lights and yellow lights change a patient's treatment. In the case of green lights, physicians altered treatment about 4 percent of the time. But these changes were very unlikely to be due to PGx information, since there is no indication in the case of a green light signal that a patient should have any issues with his or her medication based on genotype.

Rather, O'Donnell said, the 4 percent change rate can be looked at as a non-genomic baseline, indicating that the increase to eight percent among those patients given a yellow light, suggests that the PGx information is indeed influencing doctors' prescribing strategies.

Asked about their reasons for altering therapy, physicians in the study have said about 40 percent of the time that they believe they are making a more informed decision based on the available PGx information. According to O'Donnell, 98 percent have told the researchers that they would be very likely to enroll another patient into the program.

The Chicago team also published some preliminary data from the project in the American Journal of Medical Genetics in March. In that paper, the authors indicated that the study is continuing to accrue about 30 patients per month.

Though the 1,200 Patients study is not intended to assess the impact of PGx-informed prescribing on patient outcomes and whether it improves care relative to standard practices, O'Donnell said in his presentation that this remains a crucial question moving forward.

The Scan

Shape of Them All

According to BBC News, researchers have developed a protein structure database that includes much of the human proteome.

For Flu and More

The Wall Street Journal reports that several vaccine developers are working on mRNA-based vaccines for influenza.

To Boost Women

China's Ministry of Science and Technology aims to boost the number of female researchers through a new policy, reports the South China Morning Post.

Science Papers Describe Approach to Predict Chemotherapeutic Response, Role of Transcriptional Noise

In Science this week: neural network to predict chemotherapeutic response in cancer patients, and more.